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          zwdbordeaux
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                <div class="bbp-reply-header" id="post-314612">
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                  <span class="bbp-reply-post-date">
                   2011年2月28日 下午8:44
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/12277/">
                    R 在植物生态生理学中的应用–从数据管理到图表制作
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                  <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/12277/page/6/#post-314612">
                   86 楼
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                  <p>
                   再传!~
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                  <p>
                   [attachment=214612,910] [attachment=214612,911]
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                <div class="bbp-reply-header" id="post-314610">
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                   2011年2月28日 下午8:28
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                   回复：
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                    R 在植物生态生理学中的应用–从数据管理到图表制作
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                   85 楼
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                  <p>
                   最后的图如下：
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                <div class="bbp-reply-header" id="post-314609">
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                   2011年2月28日 下午8:26
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                   回复：
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                    R 在植物生态生理学中的应用–从数据管理到图表制作
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                  <p>
                   回复 第84楼 的 gtche：代码已经发到邮件,请查收.
                  </p>
                  <p>
                   此外为了方便以后有人察看,我把所有代码贴出来:
                  </p>
                  <pre class="highlight "># data analysis with R
#by Z.W Dai
#7/12/2008
#email: &lt;a href="mailto:zhwdai@yahoo.com"&gt;zhwdai@yahoo.com&lt;/a&gt;
## analysis of variance
library(MASS)
cab&lt;-data.frame(cabbages)
datam&lt;-cbind(HW=cab$HeadWt,VC=cab$VitC)
Cul&lt;-as.factor(cab$Cult)
Date&lt;-as.factor(cab$Date)
## anova for univariable
aov.1&lt;-aov(cab$HeadWt~Cul*Date)
summary(aov.1)
# anova for multiplevariable
aov.2&lt;-manova(datam~Cul*Date)
summary.aov(aov.2)
cab$comtreat&lt;-as.factor(paste(cab$Cult,cab$Date,sep=""))
################Figure.1######################
#boxplot
pdf(family='GB1',file='E:\\fig1.pdf',paper="special",width=4.5,height=7.5)
par(mfrow=c(2,1),mai=c(0,0,0,0),omi=c(0.6,0.6,0.1,0.1))
plot(cab$comtreat,cab$HeadWt,xaxt="n")
legend("topright","A",bty="n")
plot(cab$comtreat,cab$VitC)
legend("topright","B",bty="n")
mtext("品种和播种日期",side=1,adj=0.5,outer=TRUE,line=2)
mtext("重量(kg)",side=2,at=0.75,adj=0.5,outer=TRUE,line=2)
mtext("维他命含量(mg/kg)",side=2,at=0.25,adj=0.5,outer=TRUE,line=2)
dev.off()
#### The End of Fig.1#######################
# multiple comparison
library(agricolae)
DFerror&lt;-df.residual(aov.1)
MSerror&lt;-deviance(aov.1)/DFerror
cmp.1&lt;-HSD.test(cab$HeadWt,Cul,DFerror,MSerror,group=TRUE)
# mc for headweight
aov.3&lt;-aov(HeadWt~comtreat,data=cab)
summary(aov.3)
DFerror&lt;-df.residual(aov.3)
MSerror&lt;-deviance(aov.3)/DFerror
cmp.2&lt;-HSD.test(cab$HeadWt,cab$comtreat,DFerror,MSerror,group=TRUE)
groupwt&lt;-data.frame(Treat=cmp.2$trt,Cul=substr(cmp.2$trt,2,3),Date=substr(cmp.2$trt,5,6),
Meanwt=cmp.2$means,Groupwt=cmp.2$M,SEwt=cmp.2$std.err)
# mc for VC content
aov.4&lt;-aov(VitC~comtreat,data=cab)
summary(aov.4)
DFerror&lt;-df.residual(aov.4)
MSerror&lt;-deviance(aov.4)/DFerror
cmp.3&lt;-HSD.test(cab$VitC,cab$comtreat,DFerror,MSerror,group=TRUE)
groupvc&lt;-data.frame(Treat=cmp.3$trt,Cul=substr(cmp.3$trt,2,3),Date=substr(cmp.3$trt,5,6),
Meanvc=cmp.3$means,Groupvc=cmp.3$M,SEvc=cmp.3$std.err)
#merge the two file
group&lt;-merge(groupwt,groupvc,by=c("Cul","Date"))
# output the data to Excel
write.table(group,"clipboard",sep="\t")
#Transfer to matrix
matwt&lt;-matrix(data=c(group$Meanwt),nrow=3,ncol=2,dimnames=list(c("D16","D20","D22"),c("CV39","CV52")),byrow=FALSE)
matwtse&lt;-matrix(data=c(group$SEwt),nrow=3,ncol=2,dimnames=list(c("D16","D20","D22"),c("CV39","CV52")),byrow=FALSE)
matvc&lt;-matrix(data=c(group$Meanvc),nrow=3,ncol=2,dimnames=list(c("D16","D20","D22"),c("CV39","CV52")),byrow=FALSE)
matvcse&lt;-matrix(data=c(group$SEvc),nrow=3,ncol=2,dimnames=list(c("D16","D20","D22"),c("CV39","CV52")),byrow=FALSE)
################Figure.2######################
# barplot for both headweight and VC content
pdf(family='GB1',file='E:\\fig2.pdf',paper="special",width=7,height=7)
library(gplots)
## barplot according to cultivar
#barplot for headweight
par(mfcol=c(2,2),mai=c(0,0,0,0),omi=c(0.6,0.6,0.1,0.1))
ci.l&lt;-matwt-matwtse
ci.u&lt;-matwt+matwtse
barplot2(matwt,beside=TRUE,col=c("black","gray","white"),legend=rownames(matwtse),plot.ci=TRUE,ci.l=ci.l,ci.u=ci.u,ylim=c(0,5.5),xaxt="n")
box()
text(x=c(1.5,2.5,3.5,5.5,6.5,7.5),y=c((group$Meanwt+group$SEwt)*1.1),group$Groupwt)
legend("topleft","A",bty="n")
#barplot for VC content
ci.l&lt;-matvc-matvcse
ci.u&lt;-matvc+matvcse
barplot2(matvc,beside=TRUE,col=c("black","gray","white"),plot.ci=TRUE,ci.l=ci.l,ci.u=ci.u,ylim=c(0,79))
box()
text(x=c(1.5,2.5,3.5,5.5,6.5,7.5),y=c((group$Meanvc+group$SEvc)+3),group$Groupvc)
legend("topleft","C",bty="n")
## barplot according to sowing date
#transpose the above matrix
tmatwt&lt;-t(matwt)
tmatvc&lt;-t(matvc)
#barplot
barplot2(tmatwt,beside=TRUE,ylim=c(0,5.5),col=c("black","gray"),legend=rownames(tmatwt),xaxt="n",yaxt="n")
box()
legend("topleft","B",bty="n")
barplot2(tmatvc,beside=TRUE,ylim=c(0,79),col=c("black","gray"),yaxt="n")
box()
legend("topleft","D",bty="n")
mtext("品种",side=1,adj=0.5,outer=TRUE,line=2,at=0.25)
mtext("播种日期",side=1,adj=0.5,outer=TRUE,line=2,at=0.75)
mtext("重量(kg)",side=2,at=0.75,adj=0.5,outer=TRUE,line=2)
mtext("维他命含量(mg/kg)",side=2,at=0.25,adj=0.5,outer=TRUE,line=2)
dev.off()
################The End of Figure.2######################
################Figure.3######################
## line&amp;point and se
pdf(family='GB1',file='E:\\fig3.pdf',paper="special",width=4.5,height=7.5)
group$date2&lt;-rep(c(16,20,22),2)
library(plotrix)
ran.wt&lt;-range(cab$HeadWt)
ran.vc&lt;-range(cab$VitC)
par(mfrow=c(2,1),mai=c(0,0,0,0),omi=c(0.6,0.6,0.1,0.1))
plot(group$date2,group$Meanwt,type="n",ylim=ran.wt,xaxt="n")
plotCI(x=group[group$Cul=="39","date2"],y=group[group$Cul=="39","Meanwt"],liw=group[group$Cul=="39","SEwt"],uiw=group[group$Cul=="39","SEwt"],pch=2,add=TRUE)
lines(x=group[group$Cul=="39","date2"],y=group[group$Cul=="39","Meanwt"],lty=1,lwd=2)
plotCI(x=group[group$Cul=="52","date2"],y=group[group$Cul=="52","Meanwt"],liw=group[group$Cul=="52","SEwt"],uiw=group[group$Cul=="52","SEwt"],pch=0,add=TRUE)
lines(x=group[group$Cul=="52","date2"],y=group[group$Cul=="52","Meanwt"],lty=2,lwd=2)
text(16,4,"A",font=2)
legend(18,4,legend=c("CV39","CV52"),lty=c(1,2),pch=c(1,0),bty="n")
plot(group$date2,group$Meanvc,type="n",ylim=ran.vc)
plotCI(x=group[group$Cul=="39","date2"],y=group[group$Cul=="39","Meanvc"],liw=group[group$Cul=="39","SEvc"],uiw=group[group$Cul=="39","SEvc"],pch=2,add=TRUE)
lines(x=group[group$Cul=="39","date2"],y=group[group$Cul=="39","Meanvc"],lty=1,lwd=2)
plotCI(x=group[group$Cul=="52","date2"],y=group[group$Cul=="52","Meanvc"],liw=group[group$Cul=="52","SEvc"],uiw=group[group$Cul=="52","SEvc"],pch=0,add=TRUE)
lines(x=group[group$Cul=="52","date2"],y=group[group$Cul=="52","Meanvc"],lty=2,lwd=2)
text(16,80,"B",font=2)
mtext("播种日期",side=1,adj=0.5,outer=TRUE,line=2)
mtext("重量(kg)",side=2,at=0.75,adj=0.5,outer=TRUE,line=2)
mtext("维他命含量(mg/kg)",side=2,at=0.25,adj=0.5,outer=TRUE,line=2)
dev.off()
################The End of Figure.3######################
################Figure.4######################
## correlation between weight and VC content
pdf(family='GB1',file='E:\\fig4.pdf',paper="special",width=4.5,height=7.5)
par(mfrow=c(2,1),mai=c(0,0,0,0),omi=c(0.6,0.6,0.1,0.1))
#using all observed data
plot(cab$HeadWt,cab$VitC,type="n",xaxt="n")
points(cab[cab$Cul=="c39","HeadWt"],cab[cab$Cul=="c39","VitC"],pch=1)
points(cab[cab$Cul=="c52","HeadWt"],cab[cab$Cul=="c52","VitC"],pch=0)
lm1&lt;-lm(VitC~HeadWt,data=cab)
summary(lm1)
abline(lm1)
text(1.7,47, "y=77.6-7.57x\n(P&lt;0.0001,n=58)")
legend("topright",legend=c("CV39","CV52"),pch=c(1,0),title="A")
# using mean value
plot(cab$HeadWt,cab$VitC,type="n")
plotCI(x=group[group$Cul=="39","Meanwt"],y=group[group$Cul=="39","Meanvc"],err="x",liw=group[group$Cul=="39","SEwt"],uiw=group[group$Cul=="39","SEwt"],pch=2,add=TRUE)
plotCI(x=group[group$Cul=="52","Meanwt"],y=group[group$Cul=="52","Meanvc"],err="x",liw=group[group$Cul=="52","SEwt"],uiw=group[group$Cul=="52","SEwt"],pch=0,add=TRUE)
plotCI(x=group[group$Cul=="39","Meanwt"],y=group[group$Cul=="39","Meanvc"],err="y",liw=group[group$Cul=="39","SEvc"],uiw=group[group$Cul=="39","SEvc"],pch=2,add=TRUE)
plotCI(x=group[group$Cul=="52","Meanwt"],y=group[group$Cul=="52","Meanvc"],err="y",liw=group[group$Cul=="52","SEvc"],uiw=group[group$Cul=="52","SEvc"],pch=0,add=TRUE)
lm2&lt;-lm(Meanvc~Meanwt,data=group)
summary(lm2)
abline(lm2)
text(1.7,47, "y=87.5-11.4x\n(P&lt;0.05,n=58)")
legend("topright",legend="B",bty="n")
mtext("重量(kg)",side=1,at=0.5,adj=0.5,outer=TRUE,line=2)
mtext("维他命含量(mg/kg)",side=2,at=0.5,adj=0.5,outer=TRUE,line=2)
dev.off()
################The End of Figure.4######################
</pre>
                  <p>
                   欢迎批评指正！
                  </p>
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                   2011年2月24日 下午12:55
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                   回复：
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                    作图：生化指标（y）~浓度（x），且浓度点按出现的时间顺序连成线（滞后回环，Hysteresis plot）
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                  <p>
                   回复 第6楼 的 bellewong：很好的问题，期待答案！
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                   2011年2月23日 下午8:31
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/103602/">
                    作图：生化指标（y）~浓度（x），且浓度点按出现的时间顺序连成线（滞后回环，Hysteresis plot）
                   </a>
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                  <p>
                   学了一招!
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                   2011年2月23日 下午8:23
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                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/103598/">
                    [请教]双因素图例如何加误差线?
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                  <p>
                  </p>
                  <pre class="highlight ">don&lt;-read.csv("zoochorous.csv",header=TRUE)
library(sciplot)
bargraph.CI(site,seeds,group=mode,data=don,ylim=c(0,160))
box()
legend(2,150,legend=as.character(levels(don$mode)),fill=c("black","white"))
</pre>
                  <p>
                  </p>
                  <p>
                   [attachment=214384,890]
                  </p>
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                   2011年2月15日 下午9:35
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/103555/">
                    作图求助
                   </a>
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                  <p>
                   真是个好葫芦!学了一招!
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                <div class="bbp-reply-header" id="post-314068">
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                   2011年2月2日 下午6:23
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/103525/">
                    新春快乐！
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                   4 楼
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                  <p>
                   祝各位兔年大展宏”兔”
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                <div class="bbp-reply-header" id="post-311909">
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                   2010年12月8日 下午1:21
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/103126/">
                    如何确定某个样品为outlier
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                  <p>
                   多谢！
                   <br/>
                   我用Tukey多重比较作了一下均值检验，确定样品4显著小于其他三个样品。
                  </p>
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                <div class="bbp-reply-header" id="post-311768">
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                   2010年12月6日 下午7:38
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/103089/">
                    plot作图，如何设定坐标的unit大小
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                  <p>
                   plot(c(1,10^3,10^4),c(1,10^3,10^4),xaxt=”n”)
                   <br/>
                   axis(side=1,at=seq(1000,10000,by=1000))
                  </p>
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                <div class="bbp-reply-header" id="post-310492">
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                   2010年11月8日 下午9:00
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/102882/">
                    关于用R作图，求高手帮助！
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                  <p>
                   线的位置已经在后面文字的正中间,注意你的上标2占用了一定的空间,所以看来线的位置就不在B字母的中间了。
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                   2010年11月4日 下午8:43
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                   回复：
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                    相关分析
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                   6 楼
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                  <p>
                   回复 第4楼 的 reader0527：
                   <br/>
                   我是植物生态生理方向的.对于我来说,只是想看看变量之间是否存在显著相关性.
                   <br/>
                   不确定是否涉及假设检验.
                  </p>
                  <p>
                   回复 第5楼 的 谢益辉：我知道在多重假设检验的情况下需要考虑调整p值，以校正那个false discovery rate。
                   <br/>
                   但是不知道在上面这种相关性分析的过程中是否也需要采用一些调整p值得方法，若有，是什么？
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                   2010年11月4日 下午8:34
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/102857/">
                    R软件建立非线性模型
                   </a>
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                   5 楼
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                  <p>
                   回复 第3楼 的 reader0527：
                   <br/>
                   你说得确实有道理,但是我不知道MSE是否可以直接计算出来？且我不知道你采取的R方定义是哪一个，
                   <br/>
                   我知道的R方定义是 R2=1-(SSre/SStot)
                   <br/>
                   SSre 是 the sum of the squares of the distances of the points from the fit.
                   <br/>
                   SStot是 the sum of the squares of the distances of the points from a horizontal line through the mean of all Y values.
                  </p>
                  <p>
                   然后可以非常简单的计算如下：
                  </p>
                  <p>
                   回复 第4楼 的 lizijing5551：
                   <br/>
                   根据上面的定义，在你的情况下，你可以计算如下：
                  </p>
                  <pre class="highlight "># 得到fitted value
fit&lt;-fitted(Reg)
SSre&lt;-sum((z$V-fit)^2)
#或者
SSre&lt;-sum(residuals(Reg)^2)
SStot&lt;-sum((z$V-mean(z$V)^2)
R2=1-SSre/SStot
</pre>
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                  <span class="bbp-reply-post-date">
                   2010年11月3日 下午7:56
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/102857/">
                    R软件建立非线性模型
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                   2 楼
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                  <p>
                   你是不会建立模型,还是不会计算R方，还是两个都不会？
                   <br/>
                   模型的建立要根据数据和试验目的而定，
                   <br/>
                   计算R方倒是简单，
                   <br/>
                   假设你已经建立了非线性模型fnl
                   <br/>
                   之后你的模型预测（predict）或者拟合（fit）的结果为pre1
                   <br/>
                   你的观测数据为don
                   <br/>
                   那么你可以将拟合结果与观测数据作线性回归
                   <br/>
                   reg1&lt;-lm(pre1~don)
                   <br/>
                   之后
                   <br/>
                   summary(reg1)$r.squared
                   <br/>
                   就得到相应的R方了。
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                   2010年10月31日 下午4:16
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/102842/">
                    相关分析
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                  <p>
                   安装agricolae包之后，察看correlation()的帮助，
                  </p>
                  <pre class="highlight ">install.packages(agricolae)
library(agricolae)
?correlation()
library(agricolae)
# example 1
data(soil)
analysis&lt;-correlation(soil[,2:8],method="pearson")
##############results#######
$correlation
         pH    EC CaCO3    MO  CIC    P    K
pH     1.00  0.55  0.73 -0.33 0.26 0.14 0.59
EC     0.55  1.00  0.32 -0.39 0.00 0.46 0.57
CaCO3  0.73  0.32  1.00 -0.23 0.30 0.05 0.36
MO    -0.33 -0.39 -0.23  1.00 0.53 0.56 0.23
CIC    0.26  0.00  0.30  0.53 1.00 0.55 0.47
P      0.14  0.46  0.05  0.56 0.55 1.00 0.62
K      0.59  0.57  0.36  0.23 0.47 0.62 1.00
$pvalue
               pH        EC       CaCO3         MO        CIC          P          K
pH    1.000000000 0.0525330 0.004797027 0.27832165 0.38601765 0.65138915 0.03484685
EC    0.052532997 1.0000000 0.294159813 0.18740661 0.98782240 0.11113616 0.03983720
CaCO3 0.004797027 0.2941598 1.000000000 0.45551075 0.31509291 0.87385528 0.22758618
MO    0.278321645 0.1874066 0.455510753 1.00000000 0.06028063 0.04468908 0.44234137
CIC   0.386017651 0.9878224 0.315092905 0.06028063 1.00000000 0.05132886 0.10181733
P     0.651389152 0.1111362 0.873855279 0.04468908 0.05132886 1.00000000 0.02442050
K     0.034846847 0.0398372 0.227586184 0.44234137 0.10181733 0.02442050 1.00000000
$n.obs
[1] 13
</pre>
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